Applied Statistics for Life Sciences

Statistics plays a crucial role in the sciences: statistical techniques provide a means of weighing quantitative evidence derived from observation and experimentation while accounting for uncertainty. This class aims to provide a hands-on introduction to common statistical methods used almost universally across the sciences and a primer on statistical concepts. Examples from the life sciences emphasize applications with relevance to students’ majors, and students learn to perform simple analyses in R.

Read the [course syllabus] for more information.

Announcements

Test 3 will be available for the calendar day Friday 3/7/25 and due by 11:59pm PST.

Instructor: Trevor Ruiz (he/him) [email]

Learning assistant: Emi Degembe (she/they) [email]

Class meetings: 2:10pm — 4:00pm TR 005-225

Office hours and learning assistant hours:

• [OH] 1:10pm — 2:00pm TR 025-236 or Zoom [by appointment]
• [LAH] 10:10am – 11:00am MF 025-107G

Preparing for class meetings:

1. Complete any problems or other work assigned with the previous class meeting; these should be submitted by the start of class.
2. Check the course website for posted reading and materials. Readings should be skimmed in advance of class meetings and read in depth after class meetings.

Week 1 (1/6/25)

Tuesday: study design and data semantics

• [reading] Vu and Harrington 1.1 - 1.3
• [lecture] course intro; study designs and data semantics
• [lab] R basics [solutions]

Thursday: descriptive statistics

• [reading] Vu and Harrington 1.4 - 1.5
• [lecture] descriptive statistics
• [lab] descriptive statistics in R [solutions]
• [HW1] due next class [prompts] [submit] [solutions]

Week 2 (1/13/25)

Tuesday: point estimation

• [reading] Vu and Harrington 4.1
• [lecture] point estimation and sampling variability
• [lab] point and interval estimation for a population mean [solutions]
• [activity] enter your [armspan] in cm
• [HW2] due next class [prompts] [submit] [solutions]

Thursday: interval estimation

• [reading] Vu and Harrington 3.3.1, 3.3.2, and 3.3.3; and 4.2
• [lecture] confidence interval coverage and critical values
• [lab] computing critical values [solutions]
• [HW3] due Thursday 1/23 [prompts] [submit] [solutions]

Week 3 (1/21/25)

MLK Jr. Day observed 1/20/25; Tuesday follows Monday schedule

Tuesday: no class meeting

Thursday: test 1 (take home) due 11:59pm PST [study guide] [prompts] [submit] [submit corrections]

Week 4 (1/27/25)

Tuesday: one-sample inference for a population mean

• [reading] Vu and Harrington 4.3.1-4.3.4
• [lecture] intro to hypothesis testing
• [lab] one-sample $t$-tests in R [solutions]
• [HW4] finish lab activity by next class [submit]

Thursday: two-sample inference for a difference in population means

• [reading] Vu and Harrington 5.3-5.4
• [lecture] two-sample inference; statistical power
• [lab] two-sample t tests in R [solutions]
• [HW5] due next class [prompts] [submit] [solutions]

Week 5 (2/3/25)

Tuesday: analysis of variance (ANOVA)

• [reading] Vu and Harrington 5.5.1 & 5.5.2
• [lecture] Introduction to analysis of variance
• [lab] fitting ANOVA models in R [solutions]
• [HW6] due next class [prompts] [submit] [solutions]

Thursday: post-hoc inference in ANOVA

• [reading] Vu and Harrington 5.5.3 & 5.5.4
• [lecture] post hoc inference in ANOVA
• [lab] pairwise comparisons and contrasts using emmeans in R [solutions]
• [HW7] due next class [prompts] [submit] [solutions]

Week 6 (2/10/25)

Tuesday: class cancelled

Thursday: test 2 review [study guide] [practice questions] [solutions]

Week 7 (2/17/25)

Tuesday: test 2

Thursday: nonparametric inference

• [reading] van Belle et al. 8.4 and 8.5 up to 8.5.4
• [lecture] nonparametric alternatives to $t$ tests and $F$ tests
• [lab] nonparametric inference in R [solutions]
• [HW8] due next class [prompts] [submit] [solutions]

Week 8 (2/24/25)

Tuesday: inference for proportions

• [reading] Vu and Harrington 8.1 & 8.2
• [lecture] inference for population proportions
• [lab] inference for proportions in R [solutions]
• [HW9] due next class [prompts] [submit] [solutions]

Thursday: analysis of contingency tables I

• [reading] Vu and Harrington 8.3 (excluding 8.3.5), 8.5
• [lecture] tests of association in two-way tables; inference for odds ratios
• [lab] ${\chi }^2$ tests and odds ratios in R [solutions]
• [HW10] due next class [prompts] [submit]

Week 9 (3/3/25)

Tuesday: analysis of contingency tables II

• [lecture] relative risk; more tests of association
• [lab] relative risk, Fisher’s exact test, and ${\chi }^2$ tests for $I\times J$ tables [solutions]
• [HW11] (optional) due next class [prompts] [submit]

Thursday: test 3 review [study guide] [practice problems] [solutions]

• [test 3] (take-home) due Friday 3/7 11:59pm PST [prompts] [submit]

Week 10 (3/10/24)

Tuesday: simple linear regression

Thursday: inference in regression

Exam info

Scheduled tests:

• Test 1: Thursday 1/23/25 (week 3)
• Test 2: Thursday 2/13/25 (week 6) Tuesday 2/18/25 (week 7)
• Test 3: Thursday 3/6/25 (week 9)
• Final: Tuesday 3/18/25 4:10pm – 7:00pm

Study resources:

• [tutoring resources]